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  3. Intermediate Functional Programming with purrr
  • 1

    Programming with purrr

    Free

    Do lambda functions, mappers, and predicates sound scary to you? Fear no more! After refreshing your purrr memory, we will dive into functional programming 101, discover anonymous functions and predicates, and see how we can use them to clean and explore data.

  • 2

    Functional programming: from theory to practice

    Ready to go deeper with functional programming and purrr? In this chapter, we'll discover the concept of functional programming, explore error handling using including safely() and possibly(), and introduce the function compact() for cleaning your code.

  • 3

    Better code with purrr

    In this chapter, we'll use purrr to write code that is clearer, cleaner, and easier to maintain. We'll learn how to write clean functions with compose() and negate(). We'll also use partial() to compose functions by "prefilling" arguments from existing functions. Lastly, we'll introduce list-columns, which are a convenient data structure that helps us write clean code using the Tidyverse.

  • 4

    Case study

    We'll wrap up everything we know about purrr in a case study. Here, we'll use purrr to analyze data that has been scraped from Twitter. We'll use clean code to organize the data and then we'll identify Twitter influencers from the 2018 RStudio conference.

Initializing

Exercise

Another purrr refresher

You just received visit_c, the number of visits on the website during the same week, but with the old design, which was shown to 1/3 of website visitors. You now want to compare these visit_c, with two previous design, visit_a and visit_b, to know which one led to more visits of the Contact page.

Again, you'll need to turn all the visitor lists to the daily number of visits.

You've been asked to provide two insights:

  • A plot for each element
  • The total number of visits for each day, regardless design

You'll test out both map() and walk() for plotting. Both return the "side effects," that is to say, the changes in the environment (drawing plots, downloading a file, changing the working directory…), but walk() won't print anything to the console.

Instructions 1/4

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  • Create a list containing the three vectors and turn these list elements to the daily number of visits.